Data communication network or smart grid will be an integral component of future power grids for effective energy management. In this thesis, a generic model of smart grid consisting of three subnetworks, namely HAN, NAN, and WAN, is presented. While there exist a plethora of issues associated in the design of smart grid, the primary objective in this thesis is to present practical models for each of the three subnetworks of smart grid and subject them to critical packet delay analysis in order to understand the parameters that would influence the end–to–end delay. It is noted that critical packet delay in smart grid plays an important role in the operation of power grid.
In Chapter 3, a model of wireless HAN is presented and consists of a set of n EDs
(or customers) in wireless communication with an MC. The traffic generated by each ED is modeled as either High Priority (HP) or Periodic Base (PB). A single-server non– preemptive queueing model with HOL scheduling strategy is suggested for transmission of packets from EDs to MC. The queueing model is analyzed and closed-form expressions for critical packet delay are derived. It is noted that the delay is a function of: i) critical packet arrival rate; ii) service rate; iii) utilization of server; and iv) rate of arrival of non–critical packets. It is noted that the expressions derived for delay are applicable to
both wired and wireless channels. The queueing model suggested is easy-to-implement and requires minimal overhead. Although, the analysis of delay has been preformed for a two-class priority traffic, it can easily be tented to multi-class priority traffic. Analytical results agree very well with simulation results.
In Chapter 4, a practical model of wireless HAN using modified FDMA and mod- ified TDMA multiple access techniques is presented. The wireless channel between EDs and MC is modeled as Rayleigh and Nakagami. First, closed–form expressions for av- erage packet delay for modified FDMA and TDMA are derived and then used to derive upper and lower bounds on critical packet delay in HAN. It is shown that these bounds are function of: i) SINR; ii) channel fading; iii) strength of transmitted power from EDs; iv) number of EDs; v) critical packet size; vi) number of non–overlapping channels, vii) number of channels, viii) path loss component, ix) distances between electrical devices and mesh client, x) channel interference range, xi) channel capacity, xii) bandwidth of the channel, and xiii) number of time slots/frequency bands. Analytical bounds on critical packet delay are illustrated as a function of a variety of system parameters. It is noted that simulation results always lie in between upper and lower bounds on critical packet delay. An analysis of delay performance as a function of typical system parameters is also provided.
In Chapter 5, an IDCA-MAC protocol for wireless HAN is proposed. The protocol eliminates collision of packets and employs MIMO system to enhance system perfor- mance. It is observed that the critical packet delay in such a HAN is poorer by nearly 20% compared to HAN using conventional IDCA-MAC protocol. However, one of the major advantages of using IDCA-MAC protocol in HAN is that throughput can be in- creased considerably for a given value of delay.
In Chapter 6, a model of NAN referred to as Wireless Mesh Backbone Network (WMBN) is presented along with its critical packet delay analysis. For routing packets in WMBN shortest path using Voronoi tessellation is used. This routing technique is described mathematically and used for deriving closed-form expressions for upper and lower bounds on critical packet delay. Two protocols are considered in WMBN and they are: i) CSMA/CA and ii) CDMA. It is noted that the critical packet delay is a function of i) signal-to-noise ratio, ii) signal interference, iii) critical packet size, iv) number of channels, v) channel interference range, vi) path loss components, vii) channel bandwidth,
and viii) distance between MRs. One of the chief results of this chapter is that CDMA protocol is superior to CSMA/CA from the view point of critical packet delay in WMBN. In Chapter 7, a fiber-optic WAN model is presented for transporting critical packets received from NAN. A DFRS algorithm is proposed for routing within WAN and closed- from expression for mean critical packet delay is derived and the parameters that influence delay are identified and illustrated.
The end-to-end critical packet delay is the sum of delays in HAN, NAN, and WAN. That is:
dend–to–end =dHAN+dNAN+dWAN (8.1) Thus, the end-to end delay depends on the resources and models used in each of
the three subnetworks of smart grid. An experimental scenario with n EDs that have
critical packets ready at t= 0 for transmission to the control station was considered and
end-to-end critical packet delay was computed using MATLAB. The delay was examined
as a function of n. If the end–to–end delay, dend–to–end, is below 150 ms, it implies that
the critical packets reach the control station within the 150 ms, a standard for smart grid. The tolerable delay is between 10 – 150 ms [90].
As shown in Fig. 8.1, it is clear that the end–to–end delay bounds are lower
than that specified for smart grid. In both the lower and upper case scenarios, the communication systems in smart grid were able to transmit the given number of critical packets to the control station within 1-150 ms. It is noticed that there is an increasing trend in delay for both upper and lower bounds as the number of critical packets increases. Since the bounds on delay are below of 150 ms, it confirms that the proposed model has the potential to serve as smart grid. However, further analysis is required for large number of EDs and multiple MCs.